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Evaluation of a camptothetin analog, Topotecan, as an HIV "block and lock" agent

ABSTRACT: The goal of this study was to investigate transcriptome remodeling induced by treatment with a camptothetin analog, Topotecan (TPT), of a primary T cell model of HIV latency. The study aimed to determine whether TPT has a global inhibitory effect on gene expression in primary CD4+ T cells, and identify mechanisms of action of this drug as an HIV “block and lock” agent. Methods: CD4+ T cells were isolated from blood of HIV seronegative study participants (N=3) and utilized to generate an in vitro model of latent HIV infection (model developed in the Spina laboratory and previously described by Spina et al., 2013 and Soto et al., 2022). Following generation of the model, cells were treated with 10 uM Topotecan or its solvent dimethyl sulfoxide (DMSO) for 24 hours. Cells were lyzed with RLT buffer containing beta-mercaptoethanol from a RNeasy micro kit (Qiagen, Inc. cat # 74004). ERCC spikes (Thermo Fisher Scientific, Inc.) were added to cell lysates based on cell number in each sample (10 ul of 1:800 dilution per million cells). RNA was extracted and subjected to deep sequencing at the Institute for Genomics Medicine (IGM) Genomics Center at the University of California San Diego. Filtering low quality reads and removal of the 3’ adapter sequences were performed using the Trim Galore tool. Reads were mapped to the human genome (build hg38) using HISAT2, and to HIV and ERCC references using bowtie. Mapped human reads were counted against the human GENCODE annotation (v37) using HT-Seq. ERCC counts were used to determine whether TPT had global inhibitory effect on transcription in primary CD4+ T cells. Because no such effect was observed, differential gene expression analysis was performed using library EdgeR in Bioconductor R without correction of expression based on ERCC spikes. Genes with false discovery rate (FDR)-corrected p-value less than 0.05 and an absolute fold change in TPT-treated samples compared to DMSO controls greater than 2, were considered significantly modulated by TPT. Pathway and gene ontology (GO) term enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) v2022q3. Pathways and GO terms with FDR-corrected p-value less than 0.05 were considered significantly enriched for differentially expressed genes. Results: An average of 19.7 million reads per sample were acquired and mapped to the human genome (build hg38). After applying filtering criteria, 9,678 human genes were identified with the HISAT2 and HTSeq workflow. Differential expression analysis was performed between TPT- and DMSO-treated samples using EdgeR. A total of 1,749 differentially expressed genes (DEGs) were identified with FDR p-value <0.05 and an absolute fold change greater than 2; of which 522 were up- and 1227 downregulated by TPT. Pathway and GO term enrichment analysis revealed that TPT interferes with gene transcription and cell signaling pathways (e.g. T cell receptor signaling, positive regulation of JNK cascade, regulation of actin cytoskeleton and positive regulation of GTPase activity were affected by TPT treatment). Conclusion: The study demonstrated that TPT induces multiple effects on transcriptome in primary CD4+ T cells. Some of these changes may represent direct and indirect mechanisms of action of TPT as an HIV “block and lock” agent.

ORGANISM(S): Homo sapiens

PROVIDER: GSE215461 | GEO | 2023/01/20

REPOSITORIES: GEO

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Project description:Purpose: The goal of this study is to identify host genes whose expression is perturbed in primary CD4+ T cells by histone deacetylase (HDAC) inhibitors (HDACi) SAHA and RMD, which have different potencies and specificities for various HDACs. The study aims to evaluate the effects of SAHA and RMD that may promote or inhibit reactivation of HIV provirus out of latency. Methods: Peripheral blood mononuclear cells were collected from 4 HIV-seronegative donors. CD4+ T cells were isolated and utilized to generate an in vitro model of latent HIV infection (model developed in the Spina laboratory and previously described in Spina et al., 2013). Mock-infected cells were cultured in parallel to evaluate effects of SAHA and RMD that may be dependent on the exposure of cells to virus. Following generation of the model, cells were treated with SAHA, RMD or their solvent dimethyl sulfoxide (DMSO) for 24 hours. Mock-infected cells were treated in parallel. The experiment had 4 biological replicates, 6 conditions for each, for a total of 24 samples. ERCC spikes (Thermo Fisher Scientific, Inc.) were added to cell lysates based on cell number in each sample (10 ul of 1:800 dilution per million cells). Mix 1 was used for DMSO- and mix 2 for SAHA- and RMD-treated cells. After all samples were collected, RNA was extracted and subjected to deep sequencing by Expression Analysis, Inc. Sequence reads that passed quality filters were mapped using Tophat (human genome) or Bowtie (ERCC spikes and HIV) and counted using HTSeq. ERCC spikes with the same concentration in mixes 1 and 2 were utilized to remove unwanted technical variation. Any human gene which did not achieve at least 1 count per million reads in at least 4 samples or any ERCC that did not achieve at least 5 reads in at least 4 samples was discarded. Differential gene expression analysis was performed using library EdgeR in Bioconductor R. National Center for Biotechnology Information (NCBI) HIV-1 Human Interaction Database was then searched for genes that have been implicated in controlling HIV latency. EdgeR output was used to extract expression information of the genes of interest from the NCBI database to identify genes implicated in HIV latency that were modulated by SAHA and RMD. The resulting lists were manually curated to verify relevance to HIV latency, using the Description column of the NCBI database, as well as available PubMed references. Results: Using a custom built data analysis pipeline, ~100 million reads per sample were mapped to the human genome (build hg38). After applying filtering criteria, 16058 human transcripts, 19 ERCC spikes transcripts, and HIV NL4-3 transcripts were identified with the Tophat/Bowtie and HTSeq workflow. Differential expression analysis was performed between SAHA or RMD-treated and DMSO-treated cells. In addition, differential modulation of gene expression by SAHA and RMD in the model of HIV latency and mock-infected cells was assessed using EdgeR. In mock-infected cells, SAHA upregulated 3,971 genes and downregulated 2,940 genes; RMD upregulated 5,068 genes and downregulated 4,050 genes. In the model of HIV latency, SAHA upregulated 3,498 genes and downregulated 2,904 genes; RMD upregulated 5,116 genes and downregulated 4,053 genes (FDR < 0.05). SAHA modulated 6, and RMD 11 genes differentially between mock-infected cells and the model of HIV latency. Following search of the NCBI HIV-1 Human Interaction Database, 27 genes upregulated and 29 downregulated in common between SAHA and RMD were found to be relevant to regulation of HIV latency; 31 were up- and 32 downregulated by RMD only; and 6 were up- and 2 were downregulated by SAHA only. Conclusions: This study demonstrates that SAHA and RMD, which have different potencies and specificities for HDACs, modulate a set of overlapping genes implicated in regulation of HIV latency. Some of these genes may be explored as additional host targets for improving the outcomes of “shock and kill” strategies.

Project description:Purpose: Next-generation sequencing (NGS) has become a powerful microscope to study cell models of HIV. The goals of this study is to analyze latent HIV infection in the TCM model of HIV latency described in Martins et al 2015 and to evaluate potential markers for HIV infection. Methods: Peripheral blood mononuclear cells were collected from 4 healthy donors. Naive CD4 T cells were isolated and utilized to generate a model of latent HIV infection. During model generation, cells were sorted and collected for RNA extraction 10 days post infection along with their uninfected counterparts. 2 days after this, cells were activated with CD3/CD28 stimulation and collected for study for a total of 16 samples from 4 donors. After cell collection, RNA was extracted from infected cells and their uninfected counterparts for deep sequencing by Expression Analysis. Sequence reads that passed quality filters were mapped using Tophat and counted using HTSeq. In addition to human transcripts, we utilized 92 ERCC spikes as negative controls. Any human gene which did not achieve at least 1 count per million reads in at least 4 samples or any ERCC that did not achieve at least 5 reads in at least 4 samples was discarded. Differential expression for activated samples was not performed, but rather used to demonstrate upregulation of T-cell activation markers along with changing to the type and abundance of HIV transcripts produced. Results: Using an custom built data analysis pipeline, 82 million reads per sample to the human genome (build hg38) and identified 13,534 human transcripts 67 ERCC spike in transcripts, and HIV NL43 transcripts were identified with the Tophat/HTSeq workflow. Differential expression analysis was performed between uninfected and latently infected cells. 826 genes were found to be differentially expressed, 275 downregulated and 551 upregulated (FDR < 0.05) with EdgeR. GO and Pathway analysis of differential expressed genes revealed significant dysregulation of genes involved in the p53 signaling pathway. Subsequent studies with pifithrin demonstrated a reduction in latently infected cell during model generation. Conclusions: This study represents the first detailed analysis of HIV latency with this cell model using next generations sequencing. These results demonstrate that the TCM model of HIV latency of Martins et al 2015 is truly reflective of HIV latency. This study also provides a framework for which to analyze future cell models of HIV latency using next generation sequencing. Finally, this work demonstrates that the p53 signaling pathway is a key pathway dysregulated in latency for this cell model with several genes dysregulated in HIV latency.

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Evaluation of a camptothetin analog, Topotecan, as an HIV "block and lock" agent

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